Article2011

An Empirical Evaluation of Thompson Sampling

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Advanced Bandit Algorithms Research Reinforcement Learning in Robotics Simulation Techniques and Applications

Abstract

Thompson sampling is one of oldest heuristic to address the exploration / ex-ploitation trade-off, but it is surprisingly unpopular in the literature. We present here some empirical results using Thompson sampling on simulated and real data, and show that it is highly competitive. And since this heuristic is very easy to implement, we argue that it should be part of the standard baselines to compare against. 1

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References

2002Finite-time Analysis of the Multiarmed Bandit Problem 1990Multi-Armed Bandit Allocation Indices.

Cited by

2011Analysis of Thompson Sampling for the multi-armed bandit problem 2012Thompson Sampling for Contextual Bandits with Linear Payoffs 2014Learning to Optimize via Posterior Sampling 2012Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis 2018A Tutorial on Thompson Sampling 2014Predictive Entropy Search for Efficient Global Optimization of Black-box Functions 2012Further Optimal Regret Bounds for Thompson Sampling 2014Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

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